1. Field of the Invention
The present invention relates to user authentication technology using biometric information such as fingerprints, palm prints, iris patterns, facial images, voice patterns, and blood vessel patterns. The invention relates particularly to a technique applicable to 1-to-N (1-to-many) verification, in which registered feature information of an object user, out of two or more pieces of registered feature information (previously extracted from two or more pieces of biometric information and then registered/stored), is identified based on to-be-verified biometric information input by the object user.
2. Description of the Related Art
Recently, biometric verification and authentication, utilizing human biometric information, has been widely used in personal computer (PC) log-in systems, and controlled access systems. The biometric information used is, for example, fingerprints, palm prints, iris patterns, facial images, voice patterns, and blood vessel patterns. Among these various types of biometric information, fingerprint verification is one of the most practical biometric authentication techniques.
In a common type of fingerprint verification, users input their ID (Identification) information such as user names before they scan their fingerprints. In such 1-to-1 verification, fingerprint data (feature information extracted from a fingerprint image) of each user is previously registered/stored in association with the user's ID. At the time of verification, the user inputs both his ID and his fingerprint image, and registered fingerprint data that is associated with the input ID information is then read out. At the same time, to-be-verified fingerprint data is extracted from the user's input fingerprint image, and then, the registered fingerprint data and the to-be-verified fingerprint data are compared with each other, whereby the user is authenticated (the user is identified as an authorized user whose fingerprint is registered).
Therefore, in order to perform this 1-to-1 verification, it is necessary to prepare a fingerprint sensor with a means for the user to input his ID information therethrough, such as a keyboard, keypad, and ID card reader. This means that such a keyboard or a keypad needs to be installed, together with the fingerprint sensor, at a place where the fingerprint verifying system is installed (a place where the PC is installed or an entrance/exit for access control), thereby incurring some extra costs and work in the installation. In addition, users are reluctant to obtain such IDs.
In contrast to this, there is also a 1-to-N (1-to-many) verification technique. In this technique, users need to input their fingerprint alone, without inputting any ID information. With this 1-to-N verification, it is not necessary for users to obtain any ID information beforehand, and the necessity of a keyboard or a keypad for inputting the ID information is also eliminated. In typical 1-to-N verification, to-be-verified fingerprint data (to-be-verified feature information) is extracted from a to-be-verified fingerprint image input by a user who is to be verified. The to-be-verified fingerprint data thus extracted is compared with all the pieces of previously registered fingerprint data (registered feature information), until a piece of registered fingerprint data that agrees with the object to-be-verified fingerprint data is found, thereby identifying/verifying the user.
However, such 1-to-N verification has the following problems. Increases in number of registered fingerprint data pieces will cause increases in time required for comparison/verification. Thus, a technique for realizing efficient comparison/verification in a short time with high accuracy has been desired.
In view of this, there has been developed a technique for suppressing increases in comparison/verification time in 1-to-N verification. For example, the following is a technique of narrowing the number of registered fingerprint data to be subjected to comparison with the to-be-verified fingerprint data. Specifically, at enrollment, to-be-registered fingerprint data (for example, feature information including minutiae such as ridge bifurcations and end points) is extracted from a to-be-registered fingerprint image, and one or more items (for example, fingerprint pattern types, distances between minutia points) of to-be-verified sub-feature information, other than the above fingerprint data, are also extracted. On the basis of such sub-feature information, the to-be-registered fingerprint data is categorized (grouped) and registered.
After that, at the time of verification, to-be-verified feature information (to-be-verified fingerprint data) and to-be-verified sub-feature information are extracted from a to-be-verified fingerprint image input by an object user. On the basis of the thus extracted to-be-verified sub-feature information, a group of registered fingerprint data to which the to-be-verified fingerprint data is considered to belong is selected and read out, whereby the number of registered fingerprint data to be subjected to comparison with the to-be-verified fingerprint data is narrowed down.
After that, the to-be-verified fingerprint data is compared with one or more pieces of registered fingerprint data that belong to the read-out group. If any piece of registered fingerprint data that agrees to the to-be-verified fingerprint data is found, the object user is identified.
In this manner, comparison for verification is performed between the registered fingerprint data having been narrowed down by use of sub-feature information and the to-be-verified fingerprint data, so that the necessity of comparing the to-be-verified fingerprint data with all the registered fingerprint data is eliminated, thereby suppressing increases in comparison/verification time required in 1-to-N verification.
Here, generally speaking, when biometric information verification, such as fingerprint verification, is performed, biometric information input by an object user at his verification is under influence of a condition of the user (for example, how the fingertip is pressed onto the fingerprint sensor) and of environmental condition (temperature, humidity, and so on) at the time the information is input, and fluctuates and varies among input occasions. Accordingly, to-be-verified sub-feature information, which is obtained from such unstable biometric information, naturally tends to fluctuate, too, so that failure can occur in narrowing down of registered fingerprint data by use of to-be-verified sub-feature information.
Against this backdrop, the previous art employs a technique of previously categorizing (grouping) registered fingerprint data according to two or more items of sub-feature information. At user verification, two or more items of to-be-verified sub-feature information, extracted at user verification, are assessed in view of the aforementioned two or more items of to-be-verified sub-feature information, in order to prevent the occurrence of failure due to the aforementioned fluctuation.
More specifically, more than one item of to-be-verified sub-feature information is extracted at verification and is compared with the more than one item of registered sub-feature information that is used in registered fingerprint data categorization (grouping), to calculate their similarity (whether their values are close/whether they belong to the same group). The similarities obtained for the individual items of sub-feature information are multiplied by weights corresponding to the individual items, and the weighted similarities are summed to obtain an assessment value. Such an assessment value is calculated for each group of registered fingerprint data, and on the basis of the calculated assessment values, a group of registered fingerprint data to which the object to-be-verified fingerprint data is considered to belong to is selected. For example, a group whose assessment value is the greatest is selected.
At that time, in previous arts (for example, see the following patent applications 1 and 2), the values of weights by which the similarities of the different items of sub-feature information are multiplied, are set depending upon statistical values obtained from past information accumulated in an existing fingerprint database. For instance, on the basis of the past information, a successful verification rate [a rate (possibility) at which a match decision is made at verification] is calculated as the statistical value for each item of sub-feature information. Higher weights are assigned to sub-feature information with higher successful verification rates, while lower weights are assigned to sub-feature information with lower successful verification rates.
[Patent Application 1] Japanese Patent Application Publication No. HEI 9-114981
[Patent Application 2] Japanese Patent Application Publication No. HEI 9-179978
However, the above technique still has the following problem. The degree of fluctuation in input biometric information (fingerprint image) greatly varies among individual users, and the degree also depends on input occasions of the biometric information. Accordingly, if the past statistical values (successful verification rates in the past) are used as the aforementioned weights, it is impossible to reflect such interpersonal differences in the fluctuation and the degree of the fluctuation depending upon input occasions on the values of the above weights.
Against this backdrop, a technique has been desired in which weights are newly assigned, every time users input to-be-verified biometric information (fingerprint images), according to the input to-be-verified biometric information. This arrangement will make it possible to reflect the aforementioned fluctuation degrees on the weights to be assigned. Further, it resultantly becomes possible not only to prevent the occurrence of failures in narrowing down the number of registered feature information items, but also to realize more accurate and reliable narrowing down of the information (registered fingerprint data).